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An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model

Author

Listed:
  • Mustafa I. Alheety

    (Department of Mathematics, College of Education for Pure Sciences, University of Anbar, Anbar 31001, Iraq)

  • HM Nayem

    (Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA)

  • B. M. Golam Kibria

    (Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA)

Abstract

We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate the performance of the estimators, and real-world data on total national research and development expenditures by country are analyzed to illustrate the findings. Both the simulation results and real-data analysis demonstrate that the proposed estimator consistently outperforms the alternatives considered in this study.

Suggested Citation

  • Mustafa I. Alheety & HM Nayem & B. M. Golam Kibria, 2025. "An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model," Stats, MDPI, vol. 8(1), pages 1-33, February.
    • Handle: RePEc:gam:jstats:v:8:y:2025:i:1:p:16-:d:1587189
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